AI Cyber Security is helping under-resourced security operations analysts stay ahead of threats, as the number of cyberattacks grow in volume and complexity. Analysing threat intelligence from millions of research papers, blogs and news stories, AI technologies like machine learning and natural language processing are providing fast insights to cut through the large amount of daily alerts, and drastically reducing response times.
AI-powered security solutions can be implemented by businesses into their systems to protect against online and offline security issues. AI is an effective solution to protect organisations from cyberattacks, however it also enables attackers to launch automated complex attacks.
One aspect of concern in AI security is the security of machine learning systems powering decision making of companies and autonomous systems. Simple changes in inputs can cause these systems to fail, enabling attackers another attack surface. Proving that companies need to consider security when implementing AI solutions.
AI is shaping the multiple aspects of web security. We are well aware that AI Cyber Security technology presents opportunities for information/cybersecurity professionals to improve their cyber defenses and new threats as cyber attackers leverage modern, publicly available machine learning algorithms.
Organisations are able to leverage artificial intelligence to enhance their security against cyberattacks such as malware, phishing, network anomalies, and unauthorised access of sensitive data. Cyber Security tools use machine learning algorithms to learn from historical data and detect anomalies to enable organisations to prevent and manage cyberattacks effectively and efficiently. AI powered deception technology helps delay and identify cyber attackers.
AI Cyber Security technology, gives organisations new processes such as data ingestion, preparation and labeling, model training, inference validation, and production deployment. These new processes are new layers added to the organisation’s tech processes that need to be protected from attacks. In such attacks, attackers change the inputs of machine learning models to cause the model to make mistakes.
Few deep learning systems are currently in in existence or production, therefore such adversarial attacks are still a mostly theoretical threat. However, once these deep learning systems start making important decisions, the importance of these threats will increase significantly. For example,
Real-time threat intelligence services to protect your customers. Threat Feeds enhance your security solutions and expand value propositions to customers. Self-Learning AI is today capable of making decisions and taking proportionate, autonomous actions to thwart in-progress attacks. In a world where attackers are using AI to find vulnerabilities and creating deep fakes to fool humans, the ability for machines to augment humans, and even fight back, is now a necessity. If you want to improve the security of your organization but don’t know where to start, or if you still have unanswered questions, please feel free to contact us: